October 2019
Beginner to intermediate
674 pages
16h 43m
English
Content preview from Mastering pandas - Second Edition
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Different types of missing values
The following are different types of missing values:
- Not a Number (NaN): NaN is a placeholder for missing values for any data type. These can be created using numpy.nan. NaNs that are created using numpy.nan can be assigned to a nullable integer datatype. The missing value of an integer type is saved as a NaN. It is the default identifier of a missing value in Python.
- NA: NA comes mostly from R, where NA is an identifier for a missing value.
- NaT: This is equivalent to a NaN for timestamp data points.
- None: This represents missing values of data types other than numeric.
- Null: This originates when a function doesn't return a value or if the value is undefined.
- Inf: Inf is infinity—a value that is greater ...
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